Search

This paper introduces the Composite Rating Method (CRM), a novel approach for the integrated evaluation of basketball player and team performances across multiple leagues. Utilizing data from Euroleague, EuroCup, and Basketball Champions League, the presented method provides comprehensive and accurate rankings, including accounting for actions not included in personal statistics. Drawing inspiration from established methodologies such as ELO, PER, Offensive and Defensive ratings, CRM offers a balanced assessment of player and team capabilities. The paper delineates the data collection and preprocessing procedures, details the algorithmic framework of CRM, and showcases its predictive capacity. By presenting a well-rounded approach to ranking, this paper aims to contribute to the advancement of performance evaluation methods in basketball and sports in general. [ABSTRACT FROM AUTHOR]

This paper uses multi-sensor data fusion technology to design and develop a multi-sensor based basketball robot's target recognition and positioning system in an unknown environment. We establish the movement model of the basketball robot, and combine the hardware configuration of the basketball robot, analyse the methods of speed control, position control, and direction control, propose a fuzzy obstacle avoidance strategy in the basketball robot movement process, and simulate the obstacle avoidance strategy. The simulation results show that the proposed fuzzy obstacle avoidance strategy is effective. Carry out the experiment of basketball robot target calibration, build the basketball robot monitoring system, and realize data transmission and real-time image processing. Experiments show that the basketball robot can achieve target recognition and positioning tasks in an unknown environment (obstacle comparison rules). It proves the effectiveness and robustness of the proposed following algorithm. It illustrates the effectiveness of the system designed in this paper. [ABSTRACT FROM AUTHOR]

Basketball is one of the three ball games, loved by all sports enthusiasts, and shooting is the most basic means of scoring. In order to improve the scoring rate of basketball shooting, the teaching objective program and method program are combined to form an efficient teaching algorithm with comprehensive characteristics, which can improve the effect of basketball shooting training. In this paper, we design reasonable auxiliary teaching methods for basketball shooting, so that students can improve their scoring rate in daily basketball training and reduce injury when improving shooting skills. Our evaluation suggests that increasing the emphasis on the training of core muscle group is beneficial to enhance the control of core muscle group of athletes and promote the improvement of their shooting percentage. For people, without the basis of foundation and has a certain movement intermittent time, longer training time while shorter core training time were observed. Moreover, for physical quality, excellent and experienced player should be aimed at training projects, which strictly controls the intermittent time, while fully stimulates the core muscles, in order to achieve the training targets and complete the training results. We conclude that it is feasible and effective to design an auxiliary teaching algorithm for basketball shooting on the basis of object decomposition program. Furthermore, the proposed algorithm is approximately 11.23% better than the other methods in terms of higher scores. [ABSTRACT FROM AUTHOR]

Intelligent video analysis has broad application prospects. How to automatically analyze and identify human behavior in video has attracted extensive attention from researchers at home and abroad. Moreover, researching effective video behavior recognition algorithms and designing efficient behavior recognition systems has important theoretical and practical value. This paper studies the nonlinear classification technique and applies the video behavior recognition algorithm to basketball recognition. Moreover, this paper studies the classical convolutional neural network model and several improvements. In addition, this paper explains the advantages of convolutional neural networks in feature extraction compared with traditional neural networks and analyzes the performance of the algorithm by designing actual experiments. The research results show that the algorithm can quickly identify multiple players on the field, and the method can effectively deal with occlusion and other issues with high accuracy and real-time. [ABSTRACT FROM AUTHOR]

The movements of sports athletes are complex and difficult to identify with current smart technologies. Therefore, in order to improve the sports athlete recognition rate, this paper analyze the sports action recognition system based on cluster regression and improved ISA deep network. Through literature investigation, this paper chooses ISA neural network as the basis of the algorithm. At the same time, this paper analyzes the shortcomings of traditional ISA neural network, combines the sports player's motion recognition requirements to improve the traditional ISA neural network, and builds a sports player motion recognition system based on the improved ISA neural network algorithm. In addition, this paper uses the network data collection method to construct the sports player action video library and takes the basketball project as an example for analysis and identifies it through feature judgment. Finally, this paper builds experiments to perform model performance analysis. The research shows that the recognition rate of basketball action is greatly improved compared with the traditional algorithm model, the results verify that the improved ISA deep network proposed in this paper has significant effectiveness in the field of human behavior recognition research. [ABSTRACT FROM AUTHOR]

Basketball is one of the students' favorite ball games, and it is also one of the most popular sports for college students to carry out after class. Especially in recent years, with the spread of NBA culture around the world and the extensive development of CUBA in domestic colleges and universities, Yao Ming has appeared in China to compete in the NBA and achieve brilliant achievements. With the increasing investment in the number of basketball venues, basketball itself, as a sport with low dependence on venues, equipment, and people, has the characteristics of economy, convenience, and remarkable sports effect compared with other sports. College students' basketball skills, basketball awareness, and love for basketball are increasing day by day. Attribute reduction algorithm is one of the core contents of knowledge discovery, which describes whether every attribute in the attribute set of information system is necessary and how to delete unnecessary knowledge. Based on the attribute reduction algorithm, this paper studies the early warning of basketball injury risk. The basketball injury can not only make athletes unable to participate in training or competition, but even cripple or lose their lives, which hinders the normal development of sports. Therefore, we should make a comprehensive and objective analysis of sports training to find out the causes of sports injuries, so as to prevent sports injuries. This algorithm takes the attribute frequency as the heuristic information and solves the attribute selection problem when the attribute frequency is the same. [ABSTRACT FROM AUTHOR]

Despite the fact that sport plays a substantial role in people's lives, funding varies significantly from one discipline to another. For example, in Poland, women's basketball in the lower divisions, is primarily developing thanks to enthusiasts. The aim of the work was to design and implement a system for analyzing match protocols containing data about the match. Particular attention was devoted to the course of the game, i.e., the order of scoring points. This type of data is not typically stored on the official websites of basketball associations but is significant from the point of view of coaches. The obtained data can be utilized to analyze the team's game during the season, the quality of players, etc. In terms of obtaining data from match protocols, a dedicated algorithm for identifying the table was used, while a neural network was utilized to recognize the numbers (with 70% accuracy). The conducted research has shown the proposed system is well suited for data acquisition based on match protocols what implies the possibility of increasing the availability of data on the games. This will allow the development of this sport discipline. Obtained conclusions can be generalized to other disciplines, where the games are recorded in paper form. [ABSTRACT FROM AUTHOR]